{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Risk-Return Analysis and Portfolio Optimization with OpenBB\n",
    "===========================================================\n",
    "#### Description\n",
    "This notebook demonstrates advanced techniques for risk-return analysis and portfolio optimization using OpenBB. We'll explore various asset classes, implement modern portfolio theory, and utilize OpenBB's extensive financial analysis capabilities. This comprehensive guide covers:\n",
    "\n",
    "1. Data collection and preprocessing\n",
    "2. Exploratory data analysis with visualizations\n",
    "3. Risk and return calculations\n",
    "4. Efficient frontier computation\n",
    "5. Portfolio optimization techniques\n",
    "6. Advanced risk metrics (VaR, CVaR)\n",
    "7. Performance attribution\n",
    "8. Scenario analysis and stress testing\n",
    "\n",
    "By the end of this notebook, you'll have a deep understanding of how to use OpenBB for sophisticated financial analysis and portfolio management.\n",
    "\n",
    "#### Author\n",
    "[Amit Kumar](https://github.com/HmbleCreator)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/OpenBB-Finance/OpenBB/blob/develop/examples/[Notebook_Name].ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are running this notebook in Colab, you can run the following command to install the OpenBB Platform:\n",
    "\n",
    "```python\n",
    "!pip install openbb\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install openbb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies\n",
    "To run this notebook, you'll need to install the following dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pandas numpy matplotlib seaborn scipy scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "from openbb import obb\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set plotting style\n",
    "plt.style.use('default')  # Use the default Matplotlib style\n",
    "sns.set_theme(style=\"whitegrid\")  # Set Seaborn style\n",
    "sns.set_palette(\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Login "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb import obb\n",
    "obb.account.login(pat=\"Enter Your PAT Key\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data Collection and Preprocessing\n",
    "Let's collect historical data for a diverse set of asset classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Closing Prices:\n",
      "            US Large Cap  US Small Cap  International Developed  \\\n",
      "date                                                              \n",
      "2023-10-19    426.429993    168.619995                67.169998   \n",
      "2023-10-20    421.190002    166.440002                66.570000   \n",
      "2023-10-23    420.459991    165.029999                66.620003   \n",
      "2023-10-24    423.630005    166.470001                67.000000   \n",
      "2023-10-25    417.549988    163.720001                66.519997   \n",
      "\n",
      "            Emerging Markets  US Aggregate Bonds  US Treasury Bonds  \\\n",
      "date                                                                  \n",
      "2023-10-19         37.240002           91.669998          82.769997   \n",
      "2023-10-20         36.790001           92.000000          83.239998   \n",
      "2023-10-23         36.759998           92.360001          84.239998   \n",
      "2023-10-24         37.230000           92.690002          85.349998   \n",
      "2023-10-25         36.740002           92.000000          83.449997   \n",
      "\n",
      "            Real Estate        Gold  Commodities  \n",
      "date                                              \n",
      "2023-10-19    72.889999  183.089996    25.320000  \n",
      "2023-10-20    72.400002  183.589996    25.129999  \n",
      "2023-10-23    71.690002  182.970001    24.969999  \n",
      "2023-10-24    72.610001  182.949997    24.700001  \n",
      "2023-10-25    71.050003  183.720001    24.879999  \n",
      "\n",
      "Daily Returns:\n",
      "            US Large Cap  US Small Cap  International Developed  \\\n",
      "date                                                              \n",
      "2023-10-20     -0.012288     -0.012928                -0.008933   \n",
      "2023-10-23     -0.001733     -0.008472                 0.000751   \n",
      "2023-10-24      0.007539      0.008726                 0.005704   \n",
      "2023-10-25     -0.014352     -0.016519                -0.007164   \n",
      "2023-10-26     -0.011975      0.002565                -0.007216   \n",
      "\n",
      "            Emerging Markets  US Aggregate Bonds  US Treasury Bonds  \\\n",
      "date                                                                  \n",
      "2023-10-20         -0.012084            0.003600           0.005678   \n",
      "2023-10-23         -0.000816            0.003913           0.012013   \n",
      "2023-10-24          0.012786            0.003573           0.013177   \n",
      "2023-10-25         -0.013161           -0.007444          -0.022261   \n",
      "2023-10-26         -0.004899            0.006739           0.015339   \n",
      "\n",
      "            Real Estate      Gold  Commodities  \n",
      "date                                            \n",
      "2023-10-20    -0.006722  0.002731    -0.007504  \n",
      "2023-10-23    -0.009807 -0.003377    -0.006367  \n",
      "2023-10-24     0.012833 -0.000109    -0.010813  \n",
      "2023-10-25    -0.021485  0.004209     0.007287  \n",
      "2023-10-26     0.019141  0.001578    -0.008440  \n",
      "Data exported successfully to financial_data.xlsx\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from openbb import obb\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "\n",
    "# Define asset classes and their tickers\n",
    "assets = {\n",
    "    'US Large Cap': 'SPY',\n",
    "    'US Small Cap': 'IWM',\n",
    "    'International Developed': 'EFA',\n",
    "    'Emerging Markets': 'EEM',\n",
    "    'US Aggregate Bonds': 'AGG',\n",
    "    'US Treasury Bonds': 'TLT',\n",
    "    'Real Estate': 'VNQ',\n",
    "    'Gold': 'GLD',\n",
    "    'Commodities': 'DBC'\n",
    "}\n",
    "\n",
    "# Set date range\n",
    "end_date = datetime.now().date()\n",
    "start_date = end_date - timedelta(days=365)  # Adjusted to 1 year for testing\n",
    "\n",
    "# Function to fetch historical data with retries\n",
    "def fetch_historical_data(ticker, retries=3):\n",
    "    for attempt in range(retries):\n",
    "        try:\n",
    "            # Fetch historical data using the correct function and parameters\n",
    "            historical_data = obb.equity.price.historical(ticker)\n",
    "\n",
    "            # Convert the OBBject to a DataFrame using the to_dataframe() method\n",
    "            df = historical_data.to_dataframe()\n",
    "\n",
    "            # Check if DataFrame is empty and return it if not\n",
    "            if df is not None and not df.empty:\n",
    "                return df\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Attempt {attempt + 1} failed for {ticker}: {str(e)}\")\n",
    "            time.sleep(2)  # Wait before retrying\n",
    "\n",
    "    return None\n",
    "\n",
    "# Fetch data and combine into a single DataFrame\n",
    "combined_data = {}\n",
    "for asset_name, ticker in assets.items():\n",
    "    df = fetch_historical_data(ticker)\n",
    "\n",
    "    if df is not None and not df.empty:\n",
    "        # Store closing prices with asset name as key\n",
    "        combined_data[asset_name] = df['close']\n",
    "    else:\n",
    "        print(f\"No data returned for {asset_name} ({ticker}).\")\n",
    "\n",
    "# Combine all asset closing prices into a single DataFrame\n",
    "df_combined = pd.DataFrame(combined_data)\n",
    "\n",
    "# Check if we have any data\n",
    "if df_combined.empty:\n",
    "    raise ValueError(\"No data was successfully retrieved. Please check your tickers and date range.\")\n",
    "\n",
    "# Calculate daily returns with specified fill method to avoid FutureWarning\n",
    "returns = df_combined.pct_change(fill_method=None).dropna()\n",
    "\n",
    "# Display the combined DataFrame for better readability\n",
    "print(\"Combined Closing Prices:\")\n",
    "print(df_combined.head())  # Display first few rows of closing prices\n",
    "\n",
    "print(\"\\nDaily Returns:\")\n",
    "print(returns.head())  # Display first few rows of returns\n",
    "\n",
    "\n",
    "# Export the combined DataFrame to an Excel file\n",
    "output_file = \"financial_data.xlsx\"\n",
    "df_combined.to_excel(output_file)\n",
    "\n",
    "print(f\"Data exported successfully to {output_file}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exploratory Data Analysis\n",
    "Let's visualize our data to better understand the relationships between different asset classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"\" />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"\" />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heatmap data (preview): \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABLAAAAPoCAYAAADOWwfbAAAAOX...\"\n",
      "Cumulative returns data (preview): \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOX...\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import io\n",
    "import base64\n",
    "from IPython.display import HTML\n",
    "\n",
    "def fig_to_base64(fig):\n",
    "    buf = io.BytesIO()\n",
    "    fig.savefig(buf, format='png')\n",
    "    buf.seek(0)\n",
    "    return base64.b64encode(buf.getvalue()).decode('utf-8')\n",
    "\n",
    "# Correlation heatmap\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(returns.corr(), annot=True, cmap='coolwarm', linewidths=0.5)\n",
    "plt.title('Correlation Heatmap of Asset Returns')\n",
    "heatmap_base64 = fig_to_base64(plt.gcf())\n",
    "plt.close()\n",
    "\n",
    "# Cumulative returns plot\n",
    "cumulative_returns = (1 + returns).cumprod()\n",
    "plt.figure(figsize=(12, 6))\n",
    "cumulative_returns.plot()\n",
    "plt.title('Cumulative Returns of Asset Classes')\n",
    "plt.ylabel('Cumulative Return')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "cumulative_returns_base64 = fig_to_base64(plt.gcf())\n",
    "plt.close()\n",
    "\n",
    "# Display images\n",
    "display(HTML(f'<img src=\"data:image/png;base64,{heatmap_base64}\" />'))\n",
    "display(HTML(f'<img src=\"data:image/png;base64,{cumulative_returns_base64}\" />'))\n",
    "\n",
    "# Store base64 strings in variables (optional, for debugging or later use)\n",
    "heatmap_data = f'\"image/png\": \"{heatmap_base64[:50]}...\"'\n",
    "cumulative_returns_data = f'\"image/png\": \"{cumulative_returns_base64[:50]}...\"'\n",
    "\n",
    "print(\"Heatmap data (preview):\", heatmap_data)\n",
    "print(\"Cumulative returns data (preview):\", cumulative_returns_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Risk and Return Calculations\n",
    "Now let's calculate key risk and return metrics for each asset class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Return  Volatility  Sharpe Ratio\n",
      "US Large Cap             0.324541    0.123667      2.462600\n",
      "Gold                     0.327863    0.140596      2.189696\n",
      "Real Estate              0.307817    0.176679      1.629036\n",
      "International Developed  0.208347    0.132221      1.424491\n",
      "US Small Cap             0.314988    0.211940      1.391844\n",
      "Emerging Markets         0.223152    0.159792      1.271348\n",
      "US Aggregate Bonds       0.084281    0.059719      1.076378\n",
      "US Treasury Bonds        0.137840    0.151714      0.776723\n",
      "Commodities             -0.115145    0.148331     -0.911104\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amiku\\AppData\\Local\\Temp\\ipykernel_2332\\3619997391.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  (risk_return_metrics['Volatility'][i], risk_return_metrics['Return'][i]),\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Check if returns DataFrame is not empty before calculations\n",
    "if not returns.empty:\n",
    "    # Calculate annualized returns and volatility\n",
    "    annual_returns = returns.mean() * 252\n",
    "    annual_volatility = returns.std() * np.sqrt(252)\n",
    "\n",
    "    # Calculate Sharpe Ratio (assuming risk-free rate of 2%)\n",
    "    risk_free_rate = 0.02\n",
    "    sharpe_ratio = (annual_returns - risk_free_rate) / annual_volatility\n",
    "\n",
    "    # Combine metrics into a DataFrame\n",
    "    risk_return_metrics = pd.DataFrame({\n",
    "        'Return': annual_returns,\n",
    "        'Volatility': annual_volatility,\n",
    "        'Sharpe Ratio': sharpe_ratio\n",
    "    })\n",
    "\n",
    "    # Sort and display the metrics\n",
    "    print(risk_return_metrics.sort_values('Sharpe Ratio', ascending=False))\n",
    "\n",
    "    # Visualize risk-return tradeoff\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    sns.scatterplot(data=risk_return_metrics, x='Volatility', y='Return', size='Sharpe Ratio',\n",
    "                    sizes=(50, 500), legend='brief', hue='Sharpe Ratio', palette='viridis')\n",
    "\n",
    "    # Annotate points with asset names\n",
    "    for i, asset in enumerate(risk_return_metrics.index):\n",
    "        plt.annotate(asset,\n",
    "                     (risk_return_metrics['Volatility'][i], risk_return_metrics['Return'][i]),\n",
    "                     xytext=(5, 5), textcoords='offset points')\n",
    "\n",
    "    plt.title('Risk-Return Tradeoff of Various Asset Classes')\n",
    "    plt.xlabel('Risk (Annualized Volatility)')\n",
    "    plt.ylabel('Return (Annualized)')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No return data available for calculations.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Efficient Frontier Computation\n",
    "Let's compute the efficient frontier to understand the optimal risk-return tradeoffs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Portfolio Weights:\n",
      "US Large Cap: 0.5582\n",
      "US Small Cap: 0.0000\n",
      "International Developed: 0.0000\n",
      "Emerging Markets: 0.0000\n",
      "US Aggregate Bonds: 0.0000\n",
      "US Treasury Bonds: 0.0436\n",
      "Real Estate: 0.0000\n",
      "Gold: 0.3982\n",
      "Commodities: 0.0000\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "def portfolio_performance(weights, returns):\n",
    "    portfolio_return = np.sum(returns.mean() * weights) * 252  # Annualized return\n",
    "    portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))  # Annualized volatility\n",
    "    return portfolio_return, portfolio_volatility\n",
    "\n",
    "def negative_sharpe_ratio(weights, returns, risk_free_rate):\n",
    "    p_return, p_volatility = portfolio_performance(weights, returns)\n",
    "    return -(p_return - risk_free_rate) / p_volatility  # Negative Sharpe Ratio for minimization\n",
    "\n",
    "def calculate_efficient_frontier(returns, num_portfolios=1000):\n",
    "    num_assets = len(returns.columns)\n",
    "    results = np.zeros((3, num_portfolios))\n",
    "\n",
    "    for i in range(num_portfolios):\n",
    "        weights = np.random.random(num_assets)\n",
    "        weights /= np.sum(weights)  # Normalize weights\n",
    "        p_return, p_volatility = portfolio_performance(weights, returns)\n",
    "        results[0,i] = p_return\n",
    "        results[1,i] = p_volatility\n",
    "        results[2,i] = p_return / p_volatility  # Sharpe Ratio\n",
    "\n",
    "    return results.T\n",
    "\n",
    "# Calculate efficient frontier\n",
    "efficient_frontier = calculate_efficient_frontier(returns)\n",
    "\n",
    "# Find the optimal portfolio (maximum Sharpe ratio)\n",
    "num_assets = len(returns.columns)\n",
    "risk_free_rate = 0.02  # Define risk-free rate if not already defined\n",
    "args = (returns, risk_free_rate)\n",
    "constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})  # Weights sum to 1\n",
    "bound = (0.0, 1.0)\n",
    "bounds = tuple(bound for asset in range(num_assets))\n",
    "\n",
    "result = minimize(negative_sharpe_ratio, num_assets*[1./num_assets], args=args,\n",
    "                  method='SLSQP', bounds=bounds, constraints=constraints)\n",
    "\n",
    "optimal_weights = result.x\n",
    "optimal_return, optimal_volatility = portfolio_performance(optimal_weights, returns)\n",
    "\n",
    "# Plot efficient frontier\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(efficient_frontier[:,1], efficient_frontier[:,0], c=efficient_frontier[:,2], cmap='viridis')\n",
    "plt.colorbar(label='Sharpe Ratio')\n",
    "plt.xlabel('Volatility')\n",
    "plt.ylabel('Return')\n",
    "plt.title('Efficient Frontier')\n",
    "\n",
    "# Plot individual assets\n",
    "for i, asset in enumerate(returns.columns):\n",
    "    plt.scatter(risk_return_metrics.loc[asset, 'Volatility'],\n",
    "                risk_return_metrics.loc[asset, 'Return'],\n",
    "                marker='o', s=200, label=asset)\n",
    "\n",
    "# Plot optimal portfolio\n",
    "plt.scatter(optimal_volatility, optimal_return, c='red', s=200, marker='*', label='Optimal Portfolio')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Optimal Portfolio Weights:\")\n",
    "for asset, weight in zip(returns.columns, optimal_weights):\n",
    "    print(f\"{asset}: {weight:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Advanced Risk Metrics\n",
    "Let's calculate Value at Risk (VaR) and Conditional Value at Risk (CVaR) for our optimal portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% VaR: -0.0097\n",
      "95% CVaR: -0.0135\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def calculate_var_cvar(returns, weights, confidence_level=0.95):\n",
    "    # Calculate portfolio returns\n",
    "    portfolio_returns = np.sum(returns * weights, axis=1)\n",
    "\n",
    "    # Calculate VaR\n",
    "    var = np.percentile(portfolio_returns, 100 * (1 - confidence_level))\n",
    "\n",
    "    # Calculate CVaR\n",
    "    cvar = portfolio_returns[portfolio_returns <= var].mean()\n",
    "\n",
    "    return var, cvar\n",
    "\n",
    "# Check if returns DataFrame is not empty before proceeding\n",
    "if not returns.empty and optimal_weights is not None:\n",
    "    # Calculate optimal portfolio returns\n",
    "    optimal_portfolio_returns = np.sum(returns * optimal_weights, axis=1)\n",
    "\n",
    "    # Calculate 95% VaR and CVaR\n",
    "    var_95, cvar_95 = calculate_var_cvar(returns, optimal_weights)\n",
    "\n",
    "    # Print results\n",
    "    print(f\"95% VaR: {var_95:.4f}\")\n",
    "    print(f\"95% CVaR: {cvar_95:.4f}\")\n",
    "\n",
    "    # Visualize VaR and CVaR\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    sns.histplot(optimal_portfolio_returns, kde=True)\n",
    "    plt.axvline(var_95, color='r', linestyle='dashed', label='95% VaR')\n",
    "    plt.axvline(cvar_95, color='g', linestyle='dashed', label='95% CVaR')\n",
    "    plt.title('Distribution of Optimal Portfolio Returns with VaR and CVaR')\n",
    "    plt.xlabel('Daily Return')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"Returns data or optimal weights are not available for calculations.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Scenario Analysis and Stress Testing\n",
    "Let's perform a simple scenario analysis to see how our optimal portfolio would perform under different market conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Mean Return  Volatility   VaR 95%  CVaR 95%\n",
      "Base Case                 0.001439    0.011118 -0.018049 -0.022964\n",
      "Market Crash             -0.538561    0.011118 -0.558049 -0.562964\n",
      "Economic Boom             0.361439    0.011118  0.341951  0.337036\n",
      "Rising Interest Rates    -0.049824    0.002629 -0.054325 -0.055655\n",
      "Commodity Boom            0.110169    0.002985  0.105341  0.103996\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def scenario_analysis(weights, returns, scenarios):\n",
    "    # Calculate portfolio returns based on weights\n",
    "    portfolio_returns = np.sum(returns * weights, axis=1)\n",
    "    scenario_results = {}\n",
    "\n",
    "    for scenario, shock in scenarios.items():\n",
    "        # Convert shock dictionary to a Series\n",
    "        shock_series = pd.Series(shock)\n",
    "        shocked_returns = returns + shock_series\n",
    "\n",
    "        # Calculate scenario portfolio returns\n",
    "        scenario_portfolio_returns = np.sum(shocked_returns * weights, axis=1)\n",
    "\n",
    "        # Store results for each scenario\n",
    "        scenario_results[scenario] = {\n",
    "            'Mean Return': scenario_portfolio_returns.mean(),\n",
    "            'Volatility': scenario_portfolio_returns.std(),\n",
    "            'VaR 95%': np.percentile(scenario_portfolio_returns, 5),\n",
    "            'CVaR 95%': scenario_portfolio_returns[scenario_portfolio_returns <= np.percentile(scenario_portfolio_returns, 5)].mean()\n",
    "        }\n",
    "\n",
    "    return pd.DataFrame(scenario_results).T\n",
    "\n",
    "# Define scenarios\n",
    "scenarios = {\n",
    "    'Base Case': {asset: 0 for asset in returns.columns},\n",
    "    'Market Crash': {asset: -0.3 for asset in returns.columns},\n",
    "    'Economic Boom': {asset: 0.2 for asset in returns.columns},\n",
    "    'Rising Interest Rates': {'US Aggregate Bonds': -0.1, 'US Treasury Bonds': -0.15},\n",
    "    'Commodity Boom': {'Gold': 0.25, 'Commodities': 0.3}\n",
    "}\n",
    "\n",
    "# Check if returns DataFrame is not empty before proceeding\n",
    "if not returns.empty:\n",
    "    # Assuming optimal_weights is defined and returns is your DataFrame of returns\n",
    "    optimal_weights = [0.2] * len(returns.columns)  # Example weights; adjust as necessary\n",
    "\n",
    "    # Perform scenario analysis\n",
    "    scenario_results = scenario_analysis(optimal_weights, returns, scenarios)\n",
    "    print(scenario_results)\n",
    "\n",
    "    # Visualize scenario analysis results\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    scenario_results[['Mean Return', 'Volatility']].plot(kind='bar')\n",
    "    plt.title('Scenario Analysis: Mean Return and Volatility')\n",
    "    plt.xlabel('Scenario')\n",
    "    plt.ylabel('Value')\n",
    "    plt.legend(loc='best')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"Returns data is not available for calculations.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Performance Attribution\n",
    "Performance attribution helps us understand which assets contributed most to our portfolio's performance. Let's implement a simple performance attribution analysis for our optimal portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         Weight    Return  Contribution  Percent Contribution\n",
      "Gold                        0.2  0.327863      0.065573              0.180772\n",
      "US Large Cap                0.2  0.324541      0.064908              0.178940\n",
      "US Small Cap                0.2  0.314988      0.062998              0.173673\n",
      "Real Estate                 0.2  0.307817      0.061563              0.169719\n",
      "Emerging Markets            0.2  0.223152      0.044630              0.123038\n",
      "International Developed     0.2  0.208347      0.041669              0.114875\n",
      "US Treasury Bonds           0.2  0.137840      0.027568              0.076000\n",
      "US Aggregate Bonds          0.2  0.084281      0.016856              0.046469\n",
      "Commodities                 0.2 -0.115145     -0.023029             -0.063487\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"\" />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot data (preview): \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABLAAAAJYCAYAAABy5h8aAAAAOX...\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import io\n",
    "import base64\n",
    "from IPython.display import HTML\n",
    "\n",
    "def fig_to_base64(fig):\n",
    "    buf = io.BytesIO()\n",
    "    fig.savefig(buf, format='png')\n",
    "    buf.seek(0)\n",
    "    return base64.b64encode(buf.getvalue()).decode('utf-8')\n",
    "\n",
    "def performance_attribution(weights, returns):\n",
    "    # Calculate portfolio return\n",
    "    portfolio_return = np.sum(returns.mean() * weights) * 252  # Annualized return\n",
    "\n",
    "    # Calculate asset contribution to returns\n",
    "    asset_contribution = returns.mean() * weights * 252  # Annualized contribution\n",
    "    percent_contribution = asset_contribution / portfolio_return  # Contribution percentage\n",
    "\n",
    "    # Create DataFrame for attribution data\n",
    "    attribution_data = pd.DataFrame({\n",
    "        'Weight': weights,\n",
    "        'Return': returns.mean() * 252,\n",
    "        'Contribution': asset_contribution,\n",
    "        'Percent Contribution': percent_contribution\n",
    "    })\n",
    "    return attribution_data.sort_values('Percent Contribution', ascending=False)\n",
    "\n",
    "# Check if returns DataFrame is not empty before proceeding\n",
    "if not returns.empty and optimal_weights is not None:\n",
    "    # Calculate performance attribution\n",
    "    attribution = performance_attribution(optimal_weights, returns)\n",
    "    print(attribution)\n",
    "\n",
    "    # Visualize performance attribution\n",
    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
    "    attribution['Percent Contribution'].plot(kind='bar', ax=ax)\n",
    "    ax.set_title('Performance Attribution of Optimal Portfolio')\n",
    "    ax.set_xlabel('Asset')\n",
    "    ax.set_ylabel('Percent Contribution to Return')\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Convert plot to base64\n",
    "    plot_base64 = fig_to_base64(fig)\n",
    "    plt.close(fig)\n",
    "\n",
    "    # Display the plot\n",
    "    display(HTML(f'<img src=\"data:image/png;base64,{plot_base64}\" />'))\n",
    "\n",
    "    # Store base64 string in a variable (optional, for debugging or later use)\n",
    "    plot_data = f'\"image/png\": \"{plot_base64[:50]}...\"'\n",
    "    print(\"Plot data (preview):\", plot_data)\n",
    "else:\n",
    "    print(\"Returns data or optimal weights are not available for calculations.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Scenario Analysis and Stress Testing\n",
    "Now, let's expand on our previous scenario analysis to include more detailed stress testing. We'll define several economic scenarios and see how our portfolio performs under each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Mean Return  Volatility  Sharpe Ratio   VaR 95%  \\\n",
      "Base Case                 0.362737    0.176488      1.941982 -0.286522   \n",
      "Market Crash            -97.917263    0.176488   -554.922790 -6.477580   \n",
      "Economic Boom            60.842737    0.176488    344.627996  3.523360   \n",
      "Rising Interest Rates   -50.037263    0.176488   -283.629696 -3.461424   \n",
      "Geopolitical Tension    -14.757263    0.176488    -83.729521 -1.238993   \n",
      "Tech Boom                50.762737    0.176488    287.513660  2.888380   \n",
      "\n",
      "                       CVaR 95%  Max Drawdown  \n",
      "Base Case             -0.364549     -0.076392  \n",
      "Market Crash          -6.555607    -97.129013  \n",
      "Economic Boom          3.445333      0.000000  \n",
      "Rising Interest Rates -3.539450    -49.629013  \n",
      "Geopolitical Tension  -1.317019    -14.629013  \n",
      "Tech Boom              2.810353      0.000000  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def detailed_scenario_analysis(weights, returns, scenarios):\n",
    "    portfolio_returns = np.sum(returns * weights, axis=1)\n",
    "    base_performance = {\n",
    "        'Mean Return': portfolio_returns.mean() * 252,\n",
    "        'Volatility': portfolio_returns.std() * np.sqrt(252),\n",
    "        'Sharpe Ratio': (portfolio_returns.mean() * 252 - risk_free_rate) / (portfolio_returns.std() * np.sqrt(252)),\n",
    "        'VaR 95%': np.percentile(portfolio_returns, 5) * np.sqrt(252),\n",
    "        'CVaR 95%': portfolio_returns[portfolio_returns <= np.percentile(portfolio_returns, 5)].mean() * np.sqrt(252),\n",
    "        'Max Drawdown': (portfolio_returns.cumsum() - portfolio_returns.cumsum().cummax()).min()\n",
    "    }\n",
    "\n",
    "    scenario_results = {'Base Case': base_performance}\n",
    "\n",
    "    for scenario, shocks in scenarios.items():\n",
    "        shocked_returns = returns.copy()\n",
    "        for asset, shock in shocks.items():\n",
    "            shocked_returns[asset] += shock\n",
    "\n",
    "        scenario_portfolio_returns = np.sum(shocked_returns * weights, axis=1)\n",
    "        scenario_results[scenario] = {\n",
    "            'Mean Return': scenario_portfolio_returns.mean() * 252,\n",
    "            'Volatility': scenario_portfolio_returns.std() * np.sqrt(252),\n",
    "            'Sharpe Ratio': (scenario_portfolio_returns.mean() * 252 - risk_free_rate) / (scenario_portfolio_returns.std() * np.sqrt(252)),\n",
    "            'VaR 95%': np.percentile(scenario_portfolio_returns, 5) * np.sqrt(252),\n",
    "            'CVaR 95%': scenario_portfolio_returns[scenario_portfolio_returns <= np.percentile(scenario_portfolio_returns, 5)].mean() * np.sqrt(252),\n",
    "            'Max Drawdown': (scenario_portfolio_returns.cumsum() - scenario_portfolio_returns.cumsum().cummax()).min()\n",
    "        }\n",
    "\n",
    "    return pd.DataFrame(scenario_results).T\n",
    "\n",
    "# Define more detailed scenarios\n",
    "detailed_scenarios = {\n",
    "    'Market Crash': {'US Large Cap': -0.4, 'US Small Cap': -0.5, 'International Developed': -0.35, 'Emerging Markets': -0.45, 'US Aggregate Bonds': 0.05, 'US Treasury Bonds': 0.1, 'Real Estate': -0.3, 'Gold': 0.15, 'Commodities': -0.25},\n",
    "    'Economic Boom': {'US Large Cap': 0.25, 'US Small Cap': 0.3, 'International Developed': 0.2, 'Emerging Markets': 0.35, 'US Aggregate Bonds': -0.05, 'US Treasury Bonds': -0.1, 'Real Estate': 0.2, 'Gold': -0.1, 'Commodities': 0.15},\n",
    "    'Rising Interest Rates': {'US Large Cap': -0.1, 'US Small Cap': -0.15, 'International Developed': -0.05, 'Emerging Markets': -0.1, 'US Aggregate Bonds': -0.2, 'US Treasury Bonds': -0.25, 'Real Estate': -0.15, 'Gold': -0.05, 'Commodities': 0.05},\n",
    "    'Geopolitical Tension': {'US Large Cap': -0.15, 'US Small Cap': -0.2, 'International Developed': -0.25, 'Emerging Markets': -0.3, 'US Aggregate Bonds': 0.1, 'US Treasury Bonds': 0.15, 'Real Estate': -0.1, 'Gold': 0.25, 'Commodities': 0.2},\n",
    "    'Tech Boom': {'US Large Cap': 0.3, 'US Small Cap': 0.35, 'International Developed': 0.2, 'Emerging Markets': 0.25, 'US Aggregate Bonds': -0.05, 'US Treasury Bonds': -0.1, 'Real Estate': 0.1, 'Gold': -0.05, 'Commodities': 0},\n",
    "}\n",
    "\n",
    "# Run detailed scenario analysis\n",
    "detailed_scenario_results = detailed_scenario_analysis(optimal_weights, returns, detailed_scenarios)\n",
    "print(detailed_scenario_results)\n",
    "\n",
    "# Visualize scenario analysis results\n",
    "plt.figure(figsize=(15, 10))\n",
    "detailed_scenario_results[['Mean Return', 'Volatility', 'Sharpe Ratio', 'VaR 95%', 'CVaR 95%']].plot(kind='bar', subplots=True, layout=(3,2), sharex=False, figsize=(15,20))\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Visualize max drawdown for each scenario\n",
    "plt.figure(figsize=(12, 6))\n",
    "detailed_scenario_results['Max Drawdown'].plot(kind='bar')\n",
    "plt.title('Max Drawdown Under Different Scenarios')\n",
    "plt.xlabel('Scenario')\n",
    "plt.ylabel('Max Drawdown')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
